All-for-One and One-For-All: Deep learning-based feature fusion for Synthetic Speech Detection

• URL: http://arxiv.org/abs/2307.15555v1
• Date: Fri, 28 Jul 2023 13:50:25 GMT
• Title: All-for-One and One-For-All: Deep learning-based feature fusion for Synthetic Speech Detection
• Authors: Daniele Mari, Davide Salvi, Paolo Bestagini, and Simone Milani
• Abstract summary: Recent advances in deep learning and computer vision have made the synthesis and counterfeiting of multimedia content more accessible than ever. In this paper, we consider three different feature sets proposed in the literature for the synthetic speech detection task and present a model that fuses them. The system was tested on different scenarios and datasets to prove its robustness to anti-forensic attacks and its generalization capabilities.
• Score: 18.429817510387473
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in deep learning and computer vision have made the synthesis and counterfeiting of multimedia content more accessible than ever, leading to possible threats and dangers from malicious users. In the audio field, we are witnessing the growth of speech deepfake generation techniques, which solicit the development of synthetic speech detection algorithms to counter possible mischievous uses such as frauds or identity thefts. In this paper, we consider three different feature sets proposed in the literature for the synthetic speech detection task and present a model that fuses them, achieving overall better performances with respect to the state-of-the-art solutions. The system was tested on different scenarios and datasets to prove its robustness to anti-forensic attacks and its generalization capabilities.

Related papers

• Understanding Audiovisual Deepfake Detection: Techniques, Challenges, Human Factors and Perceptual Insights [49.81915942821647]
  Deep Learning has been successfully applied in diverse fields, and its impact on deepfake detection is no exception. Deepfakes are fake yet realistic synthetic content that can be used deceitfully for political impersonation, phishing, slandering, or spreading misinformation. This paper aims to improve the effectiveness of deepfake detection strategies and guide future research in cybersecurity and media integrity.
  arXiv  Detail & Related papers  (2024-11-12T09:02:11Z)
• SONAR: A Synthetic AI-Audio Detection Framework and Benchmark [59.09338266364506]
  SONAR is a synthetic AI-Audio Detection Framework and Benchmark. It aims to provide a comprehensive evaluation for distinguishing cutting-edge AI-synthesized auditory content. It is the first framework to uniformly benchmark AI-audio detection across both traditional and foundation model-based deepfake detection systems.
  arXiv  Detail & Related papers  (2024-10-06T01:03:42Z)
• A Multimodal Framework for Deepfake Detection [0.0]
  Deepfakes, synthetic media created using AI, can convincingly alter videos and audio to misrepresent reality. Our research addresses the critical issue of deepfakes through an innovative multimodal approach. Our framework combines visual and auditory analyses, yielding an accuracy of 94%.
  arXiv  Detail & Related papers  (2024-10-04T14:59:10Z)
• Leveraging Mixture of Experts for Improved Speech Deepfake Detection [53.69740463004446]
  Speech deepfakes pose a significant threat to personal security and content authenticity. We introduce a novel approach for enhancing speech deepfake detection performance using a Mixture of Experts architecture.
  arXiv  Detail & Related papers  (2024-09-24T13:24:03Z)
• Audio Anti-Spoofing Detection: A Survey [7.3348524333159]
  Deep learning has given rise to sophisticated algorithms capable of manipulating or creating multimedia fake content, known as Deepfake. Audio anti-spoofing detection challenges have been organized to foster the development of anti-spoofing countermeasures. This survey paper presents a comprehensive review of every component within the detection pipeline, including algorithm architectures, optimization techniques, application generalizability, evaluation metrics, performance comparisons, available datasets, and open-source availability.
  arXiv  Detail & Related papers  (2024-04-22T06:52:12Z)
• NPVForensics: Jointing Non-critical Phonemes and Visemes for Deepfake Detection [50.33525966541906]
  Existing multimodal detection methods capture audio-visual inconsistencies to expose Deepfake videos. We propose a novel Deepfake detection method to mine the correlation between Non-critical Phonemes and Visemes, termed NPVForensics. Our model can be easily adapted to the downstream Deepfake datasets with fine-tuning.
  arXiv  Detail & Related papers  (2023-06-12T06:06:05Z)
• A Vector Quantized Approach for Text to Speech Synthesis on Real-World Spontaneous Speech [94.64927912924087]
  We train TTS systems using real-world speech from YouTube and podcasts. Recent Text-to-Speech architecture is designed for multiple code generation and monotonic alignment. We show thatRecent Text-to-Speech architecture outperforms existing TTS systems in several objective and subjective measures.
  arXiv  Detail & Related papers  (2023-02-08T17:34:32Z)
• Combining Automatic Speaker Verification and Prosody Analysis for Synthetic Speech Detection [15.884911752869437]
  We present a novel approach for synthetic speech detection, exploiting the combination of two high-level semantic properties of the human voice. On one side, we focus on speaker identity cues and represent them as speaker embeddings extracted using a state-of-the-art method for the automatic speaker verification task. On the other side, voice prosody, intended as variations in rhythm, pitch or accent in speech, is extracted through a specialized encoder.
  arXiv  Detail & Related papers  (2022-10-31T11:03:03Z)
• Deepfake audio detection by speaker verification [79.99653758293277]
  We propose a new detection approach that leverages only the biometric characteristics of the speaker, with no reference to specific manipulations. The proposed approach can be implemented based on off-the-shelf speaker verification tools. We test several such solutions on three popular test sets, obtaining good performance, high generalization ability, and high robustness to audio impairment.
  arXiv  Detail & Related papers  (2022-09-28T13:46:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.